Method for tracking multiple target objects, device, and computer program for implementing the tracking of multiple target objects for the case of moving objects

1. A method for tracking multiple target objects, moving objects to be tracked being projected onto a grid map having grid cells, the method comprising:

computing a velocity distribution for a next time step with a transitional velocity distribution, which indicates how the objects associated with a grid cell move from one time step to the next time step, based on the preceding velocity distribution;

calculating, for each of the grid cells, a transitional probability information item, which indicates, for objects in each of the grid cells, probabilities of the objects reaching possible, further grid cells, as a function of the velocity distribution;

calculating an occupancy probability for each of the grid cells for a subsequent time step, based on transitional probability information item; and

operating a system as a function of the occupancy probabilities for the grid cells wherein:

the transitional velocity distribution for each of the grid cells is calculated as a function of a supplied plan, with a trained neural network and with a multivariate Gaussian model,

the transitional velocity distribution is ascertained from an acceleration distribution, with a neighboring transitional velocity distribution obtained with the aid of the neural network, using the multivariate Gaussian model, and

the transitional velocity distribution for a particular grid cell is ascertained, in that:

an acceleration distribution is ascertained from the neighboring transitional velocity distribution by subtraction;

the acceleration distribution is expanded to a range of grid cells with the aid of the multivariate Gaussian model, and rendered discrete for grid cells situated about the particular grid cell; and

the velocity distribution for the next time step from the expanded acceleration distribution in the grid cells situated about the particular grid cell is ascertained;

a robot is controlled as a function of the occupancy probabilities for the grid cells.

2. The method of claim 1 , wherein a velocity distribution is initially provided for each of the grid cells.

3. The method of claim 1 , wherein the neural network is provided, in that:

measurement data regarding occupancies of grid cells over a number of time steps are provided in one or more training environments, which are described by one or more training plans;

for each of the grid cells, frequencies for each combination of occupancies of adjacent grid cells are determined, the frequencies each indicating how often the condition is satisfied, that a particular, adjacent grid cell is occupied in the preceding time step, that the particular grid cell is occupied in the current time step, and that a particular, adjacent grid cell is occupied in a subsequent time step;

probabilities are ascertained from the frequencies for each of the grid cells, so as to obtain a neighboring transitional velocity distribution for training; and

the neural network, in particular, of a convolutional neural network, is trained, using the neighboring transitional velocity distribution for training and the one or more training plans.

4. The method of claim 3 , wherein the neighboring transitional velocity distribution for each of the grid cells of the grid map of the defined local environment is modeled as a function of the specified plan and the trained neural network.

5. The method of claim 1 , wherein the measurement indicates an occupancy and/or velocity of one or more of the grid cells in a current time step, and the occupancy probability is corrected based on the measurement.

6. An apparatus for executing a method of tracking multiple target objects, moving objects to be tracked being projected onto a grid map having grid cells, comprising:

a device configured to perform the following:

calculating a velocity distribution for the next time step with a transition velocity distribution, which indicates how the objects associated with a particular grid cell move from one time step to the next, based on the preceding velocity distribution;

calculating, for each of the grid cells, a transitional probability information item, which indicates, for objects in each of the grid cells, probabilities of the objects reaching possible, further grid cells, as a function of the velocity distribution;

calculating an occupancy probability for each of the grid cells for a subsequent time step, based on the transitional probability information item; and

operating a system as a function of the occupancy probabilities for the grid cells wherein:

the transitional velocity distribution for each of the grid cells is calculated as a function of a supplied plan, with a trained neural network and with a multivariate Gaussian model, and

the transitional velocity distribution is ascertained from an acceleration distribution, with a neighboring transitional velocity distribution obtained with the aid of the neural network, using the multivariate Gaussian model, and

the transitional velocity distribution for a particular grid cell is ascertained, in that:

an acceleration distribution is ascertained from the neighboring transitional velocity distribution by subtraction;

the acceleration distribution is expanded to a range of grid cells with the aid of the multivariate Gaussian model, and rendered discrete for grid cells situated about the particular grid cell; and

the velocity distribution for the next time step from the expanded acceleration distribution in the grid cells situated about the particular grid cell is ascertained;

a robot is controlled as a function of the occupancy probabilities for the grid cells.

7. A non-transitory computer readable medium having a computer program, which is executable by a processor, comprising:

a program code arrangement having program code for tracking multiple target objects, moving objects to be tracked being projected onto a grid map having grid cells, tby performing the following:

computing a velocity distribution for a next time step with a transitional velocity distribution, which indicates how the objects associated with a grid cell move from one time step to the next time step, based on the preceding velocity distribution;

calculating, for each of the grid cells, a transitional probability information item, which indicates, for objects in each of the grid cells, probabilities of the objects reaching possible, further grid cells, as a function of the velocity distribution;

calculating an occupancy probability for each of the grid cells for a subsequent time step, based on transitional probability information item; and

operating a system as a function of the occupancy probabilities for the grid cells wherein:

the transitional velocity distribution for each of the grid cells is calculated as a function of a supplied plan, with a trained neural network and with a multivariate Gaussian model, and

the transitional velocity distribution is ascertained from an acceleration distribution, with a neighboring transitional velocity distribution obtained with the aid of the neural network, using the multivariate Gaussian model, and

the transitional velocity distribution for a particular grid cell is ascertained, in that:

an acceleration distribution is ascertained from the neighboring transitional velocity distribution by subtraction;

the acceleration distribution is expanded to a range of grid cells with the aid of the multivariate Gaussian model, and rendered discrete for grid cells situated about the particular grid cell; and

the velocity distribution for the next time step from the expanded acceleration distribution in the grid cells situated about the particular grid cell is ascertained;

a robot is controlled as a function of the occupancy probabilities for the grid cells.

8. The computer readable medium of claim 7 , wherein a velocity distribution is initially provided for each of the grid cells.